0000000000480080
AUTHOR
Irène Foucherot
The PLVC display color characterization model revisited
This work proposes a study of the Piecewise Linear assuming Variation in Chromaticity (PLVC) dis- play color characterization model. This model has not been widely used as the improved accuracy compared with the more common PLCC (Piecewise Linear assuming Chromaticity Constancy) model is not significant for CRT (Cathode Ray Tube) display technology, and it requires more computing power than this model. With today's computers, computational complexity is less of a problem, and today's display technologies show a different colori- metric behavior than CRTs. The main contribution of this work is to generalize the PLVC model to multiprimary displays and to provide extensive experimental results…
New methods for analysing colour texture based on the Karhunen–Loeve transform and quantification
In this article, we offer an original study on the analysis of the texture of colour images based on Local Linear Transforms (LLT). Our colour approach is based on the separability of the data which reduces the number of texture parameters. We also propose the extension of Run Lengths (RL) and Co-occurrence Matrixes (CM) to colour images. In this respect, two different ways were explored (data merging and quantification). We finally present a comparative study showing the efficiency of the first method (LLT) as well as the complementary nature of the other methods (RL, CM).
Dimensionality Reduction Techniques: An Operational Comparison On Multispectral Satellite Images Using Unsupervised Clustering
Multispectral satellite imagery provides us with useful but redundant datasets. Using Dimensionality Reduction (DR) algorithms, these datasets can be made easier to explore and to use. We present in this study an objective comparison of five DR methods, by evaluating their capacity to provide a usable input to the K-means clustering algorithm. We also suggest a method to automatically find a suitable number of classes K, using objective "cluster validity indexes" over a range of values for K. Ten Landsat images have been processed, yielding a classification rate in the 70-80% range. Our results also show that classical linear methods, though slightly outperformed by more recent nonlinear al…
Reduction of the number of spectral bands in Landsat images: a comparison of linear and nonlinear methods
We describe some applications of linear and nonlinear pro- jection methods in order to reduce the number of spectral bands in Land- sat multispectral images. The nonlinear method is curvilinear component analysis CCA, and we propose an adapted optimization of it for image processing, based on the use of principal-component analysis PCA, a linear method. The principle of CCA consists in reproducing the topol- ogy of the original space projection points in a reduced subspace, keep- ing the maximum of information. Our conclusions are: CCA is an im- provement for dimension reduction of multispectral images; CCA is really a nonlinear extension of PCA; CCA optimization through PCA called CCAinitP…
A geometrical approach for inverting display color-characterization models
— Some display color-characterization models are not easily inverted. This work proposes ways to build geometrical inverse models given any forward color-characterization model. The main contribution is to propose and analyze several methods to optimize the 3-D geometrical structure of an inverse color-characterization model directly based on the forward model. Both the amount of data and their distribution in color space is especially focused on. Several optimization criteria, related either to an evaluation data set or to the geometrical structure itself, are considered. A practical case with several display devices, combining the different methods proposed in the article, are considered …